Before pursuing Machine Learning, it is essential to follow a map that will help you in your career path. Here are the top 5 prerequisites for Machine Learning that you can consider if you are interested in Machine Learning:

**The 5 Prerequisites to Learn Machine Learning**

While Machine Learning courses do not necessarily require you to have prior skills in the domain, it eventually does get down to how well you can perform and work with programming languages, statistical means, variables, linear equations, histograms, etc. Hence, you need to be well prepared to pursue Machine Learning. Here is a short list of Machine learning prerequisites to get you going.

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**Statistics**

Statistics, as a discipline, is concerned mainly with data collection, sorting, analysis, interpretation, and presentation. Some of you might have already guessed how statistics is of value to Machine Learning. Data is, of course, a huge part of any technology today. Let’s talk about how statistics fit into all this.

When talking about statistics, there are two kinds. One is descriptive statistics, and the other is inferential statistics. Descriptive statistics, as its name suggests, is basically numbers that describe a certain dataset, i.e., it summarizes the dataset at hand into something more meaningful. Inferential statistics draw conclusions from a sample instead of the whole dataset.

A Machine Learning expert will have to be familiar with:

- Mean
- Median
- Standard deviation
- Outliers
- Histogram

**Probability**

Probability describes how likely it is for an event to occur. All data-driven decisions stem from the foundation of probability. In Machine Learning, you will be dealing with:

- Notation
- Probability distribution (joint and conditional)
- Different rules of probability (the Bayes theorem, the sum rule, and the product/chain rule)
- Independence
- Continuous random variables

These are only a few of the concepts. Machine Learning aspirants will be working with a lot more.

**Linear Algebra**

While linear algebra is integral in Machine Learning, the dynamics between the two is a little vague and is only explicable through abstract concepts of vector spaces and matrix operations. Linear algebra in Machine Learning covers concepts such as:

- Algorithms in code
- Linear transforms
- Notations
- Matrix multiplication
- Tensor and the tensor rank

**Calculus**

Calculus is crucial to building a Machine Learning model. An integral part of several Machine Learning algorithms, calculus is another way you can aim for a Machine Learning career. As an aspirant, you can familiarize yourself with:

- Basic knowledge of integration and differentiation
- Partial derivatives
- Gradient or slope
- Chain rule (for training neural networks)

**Programming Languages**

If you have a good foundation in programming, this is good news for you as Machine Learning algorithms are put into effect with code. While you could get away as a novice programmer and focus on the mathematics front, it is advisable to pick up at least one programming language as it will truly help your understanding of the internal mechanisms. However, you need to pick up a programming language that will make it easy to implement Machine Learning algorithms. Here are a few popular ones.

**Python:**

Python’s easy syntax, built-in functions, and wide package support make it popular for Machine Learning, especially for beginners. It has the most-supported libraries. Through the Python Package Index (PyPI), one can access over 235,000 packages, and not to mention, there is great community support to learn Python.

As Machine Learning with Python prerequisites, you will be learning:

- NumPy for mathematical operations
- TensorFlow for Deep Learning
- PyTorch package for Deep Learning
- OpenCV and Dlib for computer vision
- Scikit-Learn for classification and regression algorithms
- Pandas for file operations
- Matplotlib for data visualization and more

Python is, however, relatively slower than other languages and also faces multithreading struggles.

*Intellipaat’s*** ***Python for Data Science Course** will help you cover the Machine Learning prerequisites.*

**R:**

R programming is another one of the AI and Machine Learning prerequisites as widely used as Python. Various Machine Learning applications nowadays are implemented through R. It comes with good library support and graphs. Here are a few of the key packages that are supported by it:

- Kernlab and Caret for regression and classification-based operations
- DataExplorer for data exploration
- Rpart and SuperML for Machine Learning
- Mlr3 for Machine Learning workflows
- Plotly and ggplot for data visualization

R is also relatively slower than C++ and can be difficult for beginners, unlike Python.

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**C++:**

Due to its portability feature, C++ is known to be majorly employed in games and large systems. It establishes a good understanding of logic building and is the go-to programming language for building libraries. As one of the prerequisites for Machine Learning, C++ supports:

- TensorFlow and Microsoft Cognitive Toolkit (CNTK) for Deep Learning
- OpenCV for computer vision
- Shogun and mlpack for Machine Learning
- OpenNN, FANN, and DyNet for neural networks

C++ also has its shortcomings due to its syntax-oriented approach, which can be difficult for beginners. It does not have good library support as well.

**MATLAB:**

Last but not least of the programming languages to learn as Machine Learning prerequisites is MATLAB or Matrix Laboratory. It supports Machine Learning operations and is used in applications and computer vision.

MATLAB has several predefined functions in addition to the GUI. This makes it easy for learners to understand. It is not syntax-oriented. The MATLAB compiler that comes along with it helps share programs as independent apps and web apps. MATLAB supports Machine Learning in a unique way. It provides:

- Optimized and reduced coded models using AutoML
- Sensor analytics using automatic code generation and many more

Despite all of its pros, MATLAB is not readily accessible or free. Moreover, the compiler is costly to buy. Hence, it has a large target audience solely in the researchers’ community.

**Choosing the Right Programming Language**

As you have already seen, every programming language has its pros and cons. So, which one should you be learning as part of the prerequisites for Machine Learning? That really comes down to your area of interest. If you want to get into game development, C++ is the language you should consider mastering. You can also make C++ a part of your prerequisites for Machine Learning if you want to develop packages. A research-oriented professional, on the other hand, will do well with MATLAB.

In terms of Machine Learning, Python and R go neck to neck. As far as the learning path is concerned, both of these programming languages come with terrific support, especially online. Out of the two, however, Python is more preferred by those who are new to coding. Machine Learning scientists who work on sentiment analysis prioritize Python (44%) and R (11%), according to Developer Economics.

**Conclusion**

As the above are among the important prerequisites for Machine Learning, one also has to know how to work with data. It is an essential skill if you want to pursue Machine Learning seriously. In this blog, we covered the essential prerequisites of Machine Learning, along with the pros and cons of some of the most preferred programming languages for ML. To cut it short, Machine Learning requires statistics, probability, calculus, linear algebra, and knowledge of programming. It is up to you to define your Machine Learning path. Test the waters to see which modules are more up your alley, and start there!

*If you are curious to learn more, raise a question in our **Machine Learning Community**.*

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